HEAD ======= >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622
db = read_excel(path="keep11_2.xlsx")
insurance = read_excel(path="inscvgpr.xlsx")
db <- db %>%
mutate(
rxdate = ymd(rxdate),
Tx.year = year(rxdate),
Fiscal.year = case_when(
rxdate >= ymd("2017-07-01") & rxdate <= ymd("2018-06-30") ~ 2018,
rxdate >=ymd("2018-07-01") & rxdate <= ymd("2019-06-30") ~ 2019,
rxdate >=ymd("2019-07-01") & rxdate <= ymd("2020-06-30") ~ 2020,
rxdate >=ymd("2020-07-01") & rxdate <= ymd("2021-06-30") ~ 2021,
rxdate >=ymd("2021-07-01") & rxdate <= ymd("2022-06-30") ~ 2022,
rxdate >=ymd("2022-07-01") & rxdate <= ymd("2023-06-30") ~ 2023,
rxdate >=ymd("2022-07-01") & rxdate <= ymd("2024-06-30") ~ 2024
),
Distance.to.FH = zip_distance("98115",zipcode, lonlat = TRUE, units = "miles")$distance,
tx = as.integer(tx)
)
Zip.df <- reverse_zipcode(db$zipcode) %>% unique() # Read in using reverse_zipcode function in zipcodeR package
db <-
left_join(
by = "zipcode",
x = db,
y = Zip.df %>% select(zipcode, median_household_income, median_home_value, population, population_density)
)
db <-
left_join(
by = "upn",
x = db,
y = insurance %>% select(PAYOR_NAME, FINANCIAL_CLASS, upn)
)
db %>%
filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
count(Fiscal.year) %>%
ggplot + aes(Fiscal.year, n) +
geom_point(stat="identity") +
geom_line(stat="identity", color="blue") +
ylab("Total number of procedures") +
xlab(NULL) +
geom_text(aes(label=n), position=position_stack(vjust=1.05)) +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c( 2018,2019, 2020, 2021, 2022,2023)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Counts
db %>%
filter(dx.category == "MM") %>%
filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
count(rxtype, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
total = sum(n),
pct = prop.table(n) * 100,
Procedure = factor(rxtype, levels = c("Abecma","Carvykti", "Allo", "Auto"))
) %>%
ggplot + aes(Fiscal.year, n, fill = Procedure) +
geom_bar(stat="identity") +
ylab("Number of patients") +
xlab(NULL) +
geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) +
geom_text( aes(label=total, x = Fiscal.year, y = total + 7, vjust = 0), color = "#104a8e") +
geom_smooth(aes(x = Fiscal.year, y=total), method = "lm", se = FALSE, color = "#104a8e", inherit.aes = FALSE, linetype = "dashed", size = 0.5)+
ggtitle("Distribution of procedures - Multiple Myeloma") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
scale_y_continuous(breaks = c(20, 40, 60, 80, 100, 120, 140, 160, 180)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Counts
db %>%
filter(dx.category == "NHL") %>%
filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
count(rxtype, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
total = sum(n),
pct = prop.table(n) * 100,
Procedure = factor(rxtype, levels = c("Breyanzi","Yescarta", "Allo", "Auto"))
) %>%
ggplot + aes(Fiscal.year, n, fill = Procedure) +
geom_bar(stat="identity") +
ylab("Number of patients") +
xlab(NULL) +
geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) +
geom_text( aes(label=total, x = Fiscal.year, y = total + 7, vjust = 0), color = "#104a8e") +
geom_smooth(aes(x = Fiscal.year, y=total), method = "lm", se = FALSE, color = "#104a8e", inherit.aes = FALSE, linetype = "dashed", size = 0.5)+
ggtitle("Distribution of procedures - Lymphoma") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
scale_y_continuous(breaks = c(20, 40, 60, 80, 100)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Counts
db %>%
filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
mutate(
Procedure = case_when(
rxtype == "Auto" ~ "Transplant",
rxtype == "Allo" ~ "Transplant",
rxtype == "Carvykti" ~ "CAR-T",
rxtype == "Abecma" ~ "CAR-T",
rxtype == "Yescarta" ~ "CAR-T",
rxtype == "Breyanzi" ~ "CAR-T"
)
) %>%
count(Procedure, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
total = sum(n),
pct = prop.table(n) * 100,
) %>%
ggplot + aes(Fiscal.year, n, fill = Procedure) +
geom_bar(stat="identity") +
ylab("Number of patients") +
xlab(NULL) +
geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) +
geom_text( aes(label=total, x = Fiscal.year, y = total + 7, vjust = 0), color = "#104a8e") +
ggtitle("CAR-T vs Transplant") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
scale_y_continuous(breaks = c(50, 100, 150, 200, 250, 300)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Counts
db %>%
filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
filter(dx.category == "MM") %>%
filter(rxtype == "Auto") %>%
count(rxnum, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
pct = prop.table(n) * 100,
ASCT = factor(recode(rxnum, "1" = "1st", "2" = "2nd", "3" = "3rd"), levels = c("3rd", "2nd", "1st") )
) %>%
ggplot + aes(Fiscal.year, n, fill = ASCT) +
geom_bar(stat="identity") +
ylab("Number of ASCTs") +
xlab(NULL) +
geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) +
ggtitle("1st vs 2nd ASCT") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
scale_y_continuous(breaks = c(20, 40, 60, 80, 100, 120, 140, 160, 180)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Counts
db %>%
filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
filter(rxtype == "Auto" & dx.category == "MM") %>%
count(inpt, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
pct = prop.table(n) * 100,
Setting = factor(ifelse(inpt=="Y","Elective admission","Outpatient"), levels = c("Outpatient","Elective admission"))
) %>%
ggplot + aes(Fiscal.year, n, fill = Setting) +
geom_bar(stat="identity") +
ylab("Number of patients") +
xlab(NULL) +
geom_text(aes(label=paste0(n)), position=position_stack(vjust=0.5)) +
ggtitle("Inpatient vs outpatient ASCT (n)") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
scale_y_continuous(breaks = c(20, 40, 60, 80, 100, 120, 140)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Proportion
db %>%
filter(rxtype == "Auto" & dx.category == "MM") %>%
count(inpt, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
pct = prop.table(n) * 100,
Setting = factor(ifelse(inpt=="Y","Elective admission","Outpatient"), levels = c("Outpatient","Elective admission"))
) %>%
ggplot + aes(Fiscal.year, pct, fill = Setting) +
geom_bar(stat="identity") +
ylab("Proportion of patients (%)") +
xlab(NULL) +
geom_text(aes(label=paste0(sprintf("%1.1f", pct),"%")), position=position_stack(vjust=0.5)) +
ggtitle("Inpatient vs outpatient ASCT (%)") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
scale_y_continuous(breaks = c(20, 40, 60, 80, 100)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
db %>%
group_by(Fiscal.year) %>%
summarise(Inpatient.duration = median(inday, na.rm=TRUE), sd = sd(inday, na.rm = TRUE), n=n() ) %>%
ggplot + aes(Fiscal.year, Inpatient.duration) +
geom_bar(stat="identity") +
geom_errorbar(aes(x=Fiscal.year, ymin = Inpatient.duration- (sd/sqrt(n) ), ymax=Inpatient.duration+(sd/sqrt(n) ) ), width=0.4, color = "black", alpha=0.9) +
ylab("Inpatient duration, days (median)") +
xlab(NULL) +
geom_text(aes(label=paste0(Inpatient.duration)), position=position_stack(vjust=0.5), color = "white") +
ggtitle("Total inpatient duration") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
scale_y_continuous(breaks = c(2, 4, 6, 8, 10, 12, 14) ) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
db %>%
group_by(Fiscal.year) %>%
summarise(Distance = median(Distance.to.FH, na.rm = TRUE), sd = sd(Distance.to.FH, na.rm = TRUE), n=n()) %>%
ggplot + aes(Fiscal.year, Distance) +
geom_bar(stat="identity") +
#geom_errorbar(aes(x=Fiscal.year, ymin = Distance- (sd/sqrt(n) ), ymax=Distance+(sd/sqrt(n) ) ), width=0.4, color = "orange", alpha=0.9) +
ylab("Distance to FHCC, miles (median)") +
xlab(NULL) +
geom_text(aes(label=signif(Distance,3)), position=position_stack(vjust=0.5), color = "white") +
ggtitle("Distance to FHCC") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
#scale_y_continuous(breaks = c(2, 4, 6, 8, 10, 12, 14) ) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Counts
db %>%
filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
mutate(
Age.category = case_when(
rxage <50 ~ "<50",
rxage >=50 & rxage <55 ~ "50-55",
rxage >=55 & rxage <60 ~ "55-60",
rxage >=60 & rxage <65 ~ "60-65",
rxage >=65 ~ "\u226565",
.default = "other"
)
) %>%
count(Age.category, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
pct = prop.table(n) * 100,
Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
) %>%
ggplot + aes(Fiscal.year, n, fill = Age) +
geom_bar(stat="identity") +
ylab("Number of patients") +
xlab(NULL) +
geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) +
ggtitle("Distribution of age (n)") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2019, 2020, 2021, 2022, 2023)) +
scale_y_continuous(breaks = c(50, 100, 150, 200, 250, 300, 350, 400)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Proportion
db %>%
mutate(
Age.category = case_when(
rxage <50 ~ "<50",
rxage >=50 & rxage <55 ~ "50-55",
rxage >=55 & rxage <60 ~ "55-60",
rxage >=60 & rxage <65 ~ "60-65",
rxage >=65 ~ "\u226565",
.default = "other"
)
) %>%
count(Age.category, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
pct = prop.table(n) * 100,
Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
) %>%
ggplot + aes(Fiscal.year, pct, fill = Age) +
geom_bar(stat="identity") +
ylab("Proportion of patients (%)") +
xlab(NULL) +
geom_text(aes(label=paste0(sprintf("%1.1f", pct),"%")), position=position_stack(vjust=0.5)) +
ggtitle("Distribution of age (%)") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
scale_y_continuous(breaks = c(20,40,60,80,100)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Counts
db %>%
filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
filter(dx.category == "MM") %>%
mutate(
Age.category = case_when(
rxage <50 ~ "<50",
rxage >=50 & rxage <55 ~ "50-55",
rxage >=55 & rxage <60 ~ "55-60",
rxage >=60 & rxage <65 ~ "60-65",
rxage >=65 ~ "\u226565",
.default = "other"
)
) %>%
count(Age.category, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
pct = prop.table(n) * 100,
Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
) %>%
ggplot + aes(Fiscal.year, n, fill = Age) +
geom_bar(stat="identity") +
ylab("Number of patients") +
xlab(NULL) +
geom_text(aes(label=paste0(n) ), position=position_stack(vjust=0.5)) +
ggtitle("Distribution of age (n)") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
scale_y_continuous(breaks = c(50, 100, 150, 200, 250, 300, 350, 400)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Proportion
db %>%
filter(dx.category == "MM") %>%
mutate(
Age.category = case_when(
rxage <50 ~ "<50",
rxage >=50 & rxage <55 ~ "50-55",
rxage >=55 & rxage <60 ~ "55-60",
rxage >=60 & rxage <65 ~ "60-65",
rxage >=65 ~ "\u226565",
.default = "other"
)
) %>%
count(Age.category, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
pct = prop.table(n) * 100,
Age = factor(Age.category, levels = c("<50", "50-55","55-60","60-65","\u226565") )
) %>%
ggplot + aes(Fiscal.year, pct, fill = Age) +
geom_bar(stat="identity") +
ylab("Proportion of patients (%)") +
xlab(NULL) +
geom_text(aes(label=paste0(sprintf("%1.1f", pct),"%")), position=position_stack(vjust=0.5)) +
ggtitle("Distribution of age (%)") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023)) +
scale_y_continuous(breaks = c(20,40,60,80,100)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Counts
db %>%
filter(Fiscal.year != 2018 & Fiscal.year != 2024) %>%
count(sex, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
pct = prop.table(n) * 100,
Sex = sex
) %>%
ggplot + aes(Fiscal.year, n, fill = Sex) +
geom_bar(stat="identity") +
ylab("Number of patients") +
xlab(NULL) +
geom_text(aes(label=paste0(n,sprintf("\n(%1.1f", pct),"%)")), position=position_stack(vjust=0.5)) +
ggtitle("Distribution of sex (n)") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2019, 2020, 2021, 2022, 2023)) +
scale_y_continuous(breaks = c(20,40,60,80,100, 120, 140, 160)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
## Proportion
db %>%
count(sex, Fiscal.year) %>%
group_by(Fiscal.year) %>%
mutate(
pct = prop.table(n) * 100,
Sex = sex
) %>%
ggplot + aes(Fiscal.year, pct, fill = Sex) +
geom_bar(stat="identity") +
ylab("Proportion of patients (%)") +
xlab(NULL) +
geom_text(aes(label=paste0(n,sprintf("\n(%1.1f", pct),"%)")), position=position_stack(vjust=0.5)) +
ggtitle("Distribution of sex (%)") +
theme_classic() +
scale_fill_brewer(palette = "Pastel1") +
scale_x_continuous(breaks = c(2018, 2019, 2020, 2021, 2022, 2023, 2024)) +
scale_y_continuous(breaks = c(20,40,60,80,100)) +
theme(axis.title = element_text(size = 16), axis.text = element_text(size = 16), legend.title = element_text(size = 16),legend.text = element_text(size = 14), axis.text.x=element_text(angle = 45, hjust = 1))
theme_gtsummary_compact()
db %>%
transmute(
"Rx year" = Fiscal.year,
"Referring facility" = referring.facility
) %>%
tbl_summary(
by = "Rx year",
missing = "ifany",
sort = all_categorical() ~ "frequency",
statistic = list(
all_continuous() ~ "{median} ({p25}, {p75})",
all_categorical() ~ "{n} ({p}%)"
)
) %>%
bold_labels()
| Characteristic | <<<<<<< HEAD2018, N = 831 | 2019, N = 2221 | 2020, N = 2061 | 2021, N = 2231 | 2022, N = 2831 | 2023, N = 3211 | 2024, N = 1331 | =======2018, N = 1921 | 2019, N = 2281 | 2020, N = 1981 | 2021, N = 2551 | 2022, N = 2901 | 2023, N = 3081 | >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Referring facility | |||||||||||||
| Internal | <<<<<<< HEAD22 (39%) | 39 (24%) | 35 (22%) | 35 (21%) | 56 (23%) | 67 (24%) | 35 (35%) | ||||||
| Other | 11 (19%) | 29 (18%) | 26 (16%) | 29 (18%) | 34 (14%) | 40 (14%) | 9 (8.9%) | ||||||
| Multicare | 2 (3.5%) | 6 (3.7%) | 16 (10%) | 6 (3.6%) | 14 (5.9%) | 23 (8.1%) | 5 (5.0%) | ||||||
| Northwest Medical Specialties | 1 (1.8%) | 9 (5.6%) | 14 (8.9%) | 4 (2.4%) | 11 (4.6%) | 14 (4.9%) | 7 (6.9%) | ||||||
| Providence | 2 (3.5%) | 4 (2.5%) | 6 (3.8%) | 8 (4.8%) | 14 (5.9%) | 22 (7.7%) | 4 (4.0%) | ||||||
| Valley Med Center | 4 (7.0%) | 8 (4.9%) | 5 (3.2%) | 6 (3.6%) | 5 (2.1%) | 13 (4.6%) | 6 (5.9%) | ||||||
| Swedish | 2 (3.5%) | 7 (4.3%) | 4 (2.5%) | 3 (1.8%) | 11 (4.6%) | 4 (1.4%) | 1 (1.0%) | ||||||
| Confluence Health | 1 (1.8%) | 8 (4.9%) | 3 (1.9%) | 5 (3.0%) | 7 (2.9%) | 4 (1.4%) | 2 (2.0%) | ||||||
| Kadlec Clinic | 1 (1.8%) | 3 (1.9%) | 5 (3.2%) | 8 (4.8%) | 6 (2.5%) | 3 (1.1%) | 4 (4.0%) | ||||||
| Virginia Mason | 1 (1.8%) | 2 (1.2%) | 1 (0.6%) | 7 (4.2%) | 11 (4.6%) | 6 (2.1%) | 1 (1.0%) | ||||||
| Cancer Care Northwest | 1 (1.8%) | 2 (1.2%) | 2 (1.3%) | 1 (0.6%) | 11 (4.6%) | 10 (3.5%) | 1 (1.0%) | ||||||
| Alaska Oncology & Hematology | 0 (0%) | 2 (1.2%) | 0 (0%) | 11 (6.7%) | 3 (1.3%) | 8 (2.8%) | 1 (1.0%) | ||||||
| Peace Health | 0 (0%) | 2 (1.2%) | 4 (2.5%) | 6 (3.6%) | 4 (1.7%) | 8 (2.8%) | 1 (1.0%) | ||||||
| Everett Clinic | 0 (0%) | 0 (0%) | 8 (5.1%) | 3 (1.8%) | 5 (2.1%) | 7 (2.5%) | 1 (1.0%) | ||||||
| Skagit | 1 (1.8%) | 2 (1.2%) | 4 (2.5%) | 2 (1.2%) | 6 (2.5%) | 8 (2.8%) | 1 (1.0%) | ||||||
| Kootenai Cancer Center | 0 (0%) | 1 (0.6%) | 5 (3.2%) | 7 (4.2%) | 4 (1.7%) | 2 (0.7%) | 3 (3.0%) | ||||||
| Straub Clinic and Hospital | 0 (0%) | 4 (2.5%) | 5 (3.2%) | 1 (0.6%) | 6 (2.5%) | 5 (1.8%) | 1 (1.0%) | ||||||
| Vista Oncology | 0 (0%) | 5 (3.1%) | 0 (0%) | 4 (2.4%) | 0 (0%) | 4 (1.4%) | 3 (3.0%) | ||||||
| NW Allergy & Asthma | 0 (0%) | 2 (1.2%) | 1 (0.6%) | 2 (1.2%) | 7 (2.9%) | 2 (0.7%) | 1 (1.0%) | ||||||
| Kaiser | 1 (1.8%) | 3 (1.9%) | 0 (0%) | 5 (3.0%) | 2 (0.8%) | 1 (0.4%) | 2 (2.0%) | ||||||
| Katmai Oncology Group | 2 (3.5%) | 3 (1.9%) | 1 (0.6%) | 1 (0.6%) | 0 (0%) | 3 (1.1%) | 4 (4.0%) | ||||||
| North Star Lodge | 1 (1.8%) | 4 (2.5%) | 2 (1.3%) | 2 (1.2%) | 2 (0.8%) | 2 (0.7%) | 1 (1.0%) | ||||||
| UWNC | 0 (0%) | 2 (1.2%) | 2 (1.3%) | 2 (1.2%) | 3 (1.3%) | 2 (0.7%) | 2 (2.0%) | ||||||
| Alaska Native Medical Center | 1 (1.8%) | 0 (0%) | 1 (0.6%) | 3 (1.8%) | 3 (1.3%) | 3 (1.1%) | 0 (0%) | ||||||
| Madigan | 1 (1.8%) | 4 (2.5%) | 0 (0%) | 0 (0%) | 5 (2.1%) | 1 (0.4%) | 0 (0%) | ||||||
| Jefferson Healthcare Oncology Clini | 0 (0%) | 2 (1.2%) | 1 (0.6%) | 1 (0.6%) | 2 (0.8%) | 3 (1.1%) | 0 (0%) | ||||||
| Queen's cancer center | 0 (0%) | 2 (1.2%) | 1 (0.6%) | 0 (0%) | =======37 (28%) | 47 (27%) | 29 (19%) | 47 (23%) | 55 (22%) | 74 (29%) | |||
| Other | 25 (19%) | 32 (19%) | 22 (15%) | 35 (17%) | 38 (15%) | 26 (10%) | |||||||
| Multicare | 4 (3.0%) | 11 (6.4%) | 12 (8.1%) | 7 (3.4%) | 17 (6.7%) | 21 (8.3%) | |||||||
| Northwest Medical Specialties | 5 (3.8%) | 9 (5.2%) | 13 (8.7%) | 7 (3.4%) | 12 (4.7%) | 14 (5.5%) | |||||||
| Providence | 4 (3.0%) | 4 (2.3%) | 7 (4.7%) | 16 (7.8%) | 15 (5.9%) | 14 (5.5%) | |||||||
| Valley Med Center | 5 (3.8%) | 10 (5.8%) | 5 (3.4%) | 5 (2.4%) | 11 (4.3%) | 11 (4.3%) | |||||||
| Swedish | 7 (5.3%) | 4 (2.3%) | 2 (1.3%) | 8 (3.9%) | 9 (3.5%) | 2 (0.8%) | |||||||
| Confluence Health | 5 (3.8%) | 6 (3.5%) | 2 (1.3%) | 7 (3.4%) | 5 (2.0%) | 5 (2.0%) | |||||||
| Kadlec Clinic | 3 (2.3%) | 4 (2.3%) | 10 (6.7%) | 2 (1.0%) | 5 (2.0%) | 6 (2.4%) | |||||||
| Virginia Mason | 1 (0.8%) | 3 (1.7%) | 2 (1.3%) | 9 (4.4%) | 9 (3.5%) | 5 (2.0%) | |||||||
| Cancer Care Northwest | 2 (1.5%) | 2 (1.2%) | 1 (0.7%) | 3 (1.5%) | 16 (6.3%) | 4 (1.6%) | |||||||
| Alaska Oncology & Hematology | 0 (0%) | 2 (1.2%) | 3 (2.0%) | 9 (4.4%) | 7 (2.7%) | 4 (1.6%) | |||||||
| Peace Health | 2 (1.5%) | 1 (0.6%) | 8 (5.4%) | 4 (2.0%) | 4 (1.6%) | 6 (2.4%) | |||||||
| Everett Clinic | 0 (0%) | 4 (2.3%) | 5 (3.4%) | 2 (1.0%) | 11 (4.3%) | 2 (0.8%) | |||||||
| Skagit | 2 (1.5%) | 1 (0.6%) | 4 (2.7%) | 5 (2.4%) | 5 (2.0%) | 7 (2.8%) | |||||||
| Kootenai Cancer Center | 0 (0%) | 4 (2.3%) | 6 (4.0%) | 4 (2.0%) | 3 (1.2%) | 5 (2.0%) | |||||||
| Straub Clinic and Hospital | 0 (0%) | 7 (4.1%) | 2 (1.3%) | 4 (2.0%) | 5 (2.0%) | 4 (1.6%) | |||||||
| Vista Oncology | 3 (2.3%) | 2 (1.2%) | 3 (2.0%) | 1 (0.5%) | 1 (0.4%) | 6 (2.4%) | |||||||
| NW Allergy & Asthma | 0 (0%) | 3 (1.7%) | 1 (0.7%) | 7 (3.4%) | 1 (0.4%) | 3 (1.2%) | |||||||
| Kaiser | 4 (3.0%) | 0 (0%) | 0 (0%) | 6 (2.9%) | 2 (0.8%) | 2 (0.8%) | |||||||
| Katmai Oncology Group | 3 (2.3%) | 3 (1.7%) | 1 (0.7%) | 0 (0%) | 0 (0%) | 7 (2.8%) | |||||||
| North Star Lodge | 5 (3.8%) | 1 (0.6%) | 2 (1.3%) | 2 (1.0%) | 1 (0.4%) | 3 (1.2%) | |||||||
| UWNC | 1 (0.8%) | 2 (1.2%) | 3 (2.0%) | 1 (0.5%) | 4 (1.6%) | 2 (0.8%) | |||||||
| Alaska Native Medical Center | 1 (0.8%) | 0 (0%) | 2 (1.3%) | 4 (2.0%) | 3 (1.2%) | 1 (0.4%) | |||||||
| Madigan | 4 (3.0%) | 1 (0.6%) | 0 (0%) | 3 (1.5%) | 3 (1.2%) | 0 (0%) | |||||||
| Jefferson Healthcare Oncology Clini | 1 (0.8%) | 1 (0.6%) | 2 (1.3%) | 0 (0%) | 4 (1.6%) | 1 (0.4%) | |||||||
| Queen's cancer center | 2 (1.5%) | 1 (0.6%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 5 (2.0%) | |||||||
| St Michael | 3 (2.3%) | 3 (1.7%) | 0 (0%) | 1 (0.5%) | 0 (0%) | 2 (0.8%) | |||||||
| VA | 1 (0.8%) | 0 (0%) | 0 (0%) | 2 (1.0%) | 1 (0.4%) | 4 (1.6%) | |||||||
| Rockwood Cancer and Blood Specialty | 1 (0.8%) | 3 (1.7%) | 1 (0.7%) | 1 (0.5%) | >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb986220 (0%) | 3 (1.1%) | 3 (3.0%) | ||||||
| St Michael | 1 (1.8%) | 3 (1.9%) | 2 (1.3%) | 0 (0%) | 1 (0.4%) | 2 (0.7%) | 0 (0%) | ||||||
| VA | 0 (0%) | 1 (0.6%) | 0 (0%) | 1 (0.6%) | 1 (0.4%) | 4 (1.4%) | 1 (1.0%) | ||||||
| Rockwood Cancer and Blood Specialty | 0 (0%) | 2 (1.2%) | 2 (1.3%) | 1 (0.6%) | 1 (0.4%) | 0 (0%) | 1 (1.0%) | ||||||
| Logan Health Hematology & Oncology | 0 (0%) | 0 (0%) | <<<<<<< HEAD1 (0.6%) | 0 (0%) | 0 (0%) | 5 (1.8%) | 0 (0%) | ||||||
| Northwest Oncology and Hematology | 1 (1.8%) | 1 (0.6%) | 1 (0.6%) | 0 (0%) | 2 (0.8%) | 1 (0.4%) | 0 (0%) | 1 (0.7%) | 0 (0%) | 3 (1.2%) | 2 (0.8%) | ||
| Northwest Oncology and Hematology | 2 (1.5%) | 1 (0.6%) | 0 (0%) | 2 (1.0%) | 1 (0.4%) | 0 (0%) | |||||||
| Compass Oncology | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | <<<<<<< HEAD4 (1.4%) | 0 (0%) | 4 (1.6%) | >>>>>>> 8984848fa5e35a5b81c41082ecb2997dddb98622|||||
| Hawaii Cancer Care Waterfront Plaza | 0 (0%) | 0 (0%) | 0 (0%) | <<<<<<< HEAD1 (0.6%) | 2 (0.8%) | 1 (0.4%) | 0 (0%) | ||||||
| Unknown | 26 | 60 | 48 | 58 | 44 | 36 | 32 | 1 (0.5%) | 3 (1.2%) | 0 (0%) | |||
| Unknown | 59 | 56 | 49 | 50 | 35 | 55 | |||||||
| 1 n (%) | |||||||||||||
theme_gtsummary_compact()
db %>%
transmute(
"Rx year" = Tx.year,
"Insurance company" = PAYOR_NAME,
Insurance = FINANCIAL_CLASS
) %>%
tbl_summary(
by = "Rx year",
missing = "ifany",
sort = all_categorical() ~ "frequency",
statistic = list(
all_continuous() ~ "{median} ({p25}, {p75})",
all_categorical() ~ "{n} ({p}%)"
)
)%>%
bold_labels()
| Characteristic | 2018, N = 1921 | 2019, N = 2281 | 2020, N = 1981 | 2021, N = 2551 | 2022, N = 2901 | 2023, N = 3081 | |
|---|---|---|---|---|---|---|---|
| Insurance company | |||||||
| HB/PB BDCT | 55 (29%) | 80 (35%) | 42 (21%) | 57 (22%) | 65 (22%) | 78 (25%) | |
| MEDICARE | 50 (26%) | 33 (14%) | 44 (22%) | 62 (24%) | 56 (19%) | 64 (21%) | |
| HB/PB URN - OPTUM HEALTH | 12 (6.3%) | 18 (7.9%) | 20 (10%) | 18 (7.1%) | 24 (8.3%) | 17 (5.5%) | |
| MOLINA HEALTHCARE MEDICAID | 9 (4.7%) | 11 (4.8%) | 17 (8.6%) | 12 (4.7%) | 15 (5.2%) | 18 (5.8%) | |
| AETNA US HEALTHCARE | 12 (6.3%) | 11 (4.8%) | 7 (3.5%) | 14 (5.5%) | 18 (6.2%) | 14 (4.5%) | |
| PREMERA BLUE CROSS | 9 (4.7%) | 11 (4.8%) | 2 (1.0%) | 7 (2.8%) | 26 (9.0%) | 11 (3.6%) | |
| HEALTH NET FEDERAL SERVICES TRICARE | 1 (0.5%) | 3 (1.3%) | 9 (4.5%) | 11 (4.3%) | 5 (1.7%) | 4 (1.3%) | |
| WA MEDICAID | 2 (1.0%) | 5 (2.2%) | 3 (1.5%) | 7 (2.8%) | 8 (2.8%) | 5 (1.6%) | |
| KAISER FHP OF WA (GROUP HEALTH) | 7 (3.6%) | 3 (1.3%) | 2 (1.0%) | 3 (1.2%) | 4 (1.4%) | 9 (2.9%) | |
| BCBS OUT OF AREA | 1 (0.5%) | 3 (1.3%) | 2 (1.0%) | 5 (2.0%) | 4 (1.4%) | 10 (3.2%) | |
| GENERIC/UNLISTED | 4 (2.1%) | 8 (3.5%) | 5 (2.5%) | 1 (0.4%) | 0 (0%) | 2 (0.6%) | |
| HB/PB INTERLINK HEALTH SERVICES | 4 (2.1%) | 2 (0.9%) | 1 (0.5%) | 5 (2.0%) | 1 (0.3%) | 6 (1.9%) | |
| COMMUNITY HEALTH PLAN OF WA MEDICAID | 3 (1.6%) | 1 (0.4%) | 5 (2.5%) | 0 (0%) | 4 (1.4%) | 3 (1.0%) | |
| GRANTS AND BUDGETS | 1 (0.5%) | 3 (1.3%) | 4 (2.0%) | 7 (2.8%) | 1 (0.3%) | 0 (0%) | |
| REGENCE BLUE SHIELD | 1 (0.5%) | 2 (0.9%) | 3 (1.5%) | 2 (0.8%) | 2 (0.7%) | 6 (1.9%) | |
| COORDINATED CARE CORPORATION MEDICAID | 3 (1.6%) | 1 (0.4%) | 2 (1.0%) | 3 (1.2%) | 3 (1.0%) | 3 (1.0%) | |
| OPTUM HEALTHCARE SOLUTIONS/URN | 2 (1.0%) | 1 (0.4%) | 2 (1.0%) | 0 (0%) | 4 (1.4%) | 6 (1.9%) | |
| BCBS OF ILLINOIS - BOEING | 1 (0.5%) | 4 (1.8%) | 0 (0%) | 2 (0.8%) | 2 (0.7%) | 5 (1.6%) | |
| WELLPOINT MEDICAID | 2 (1.0%) | 5 (2.2%) | 1 (0.5%) | 0 (0%) | 5 (1.7%) | 1 (0.3%) | |
| ALASKA MEDICAID | 1 (0.5%) | 0 (0%) | 2 (1.0%) | 3 (1.2%) | 2 (0.7%) | 3 (1.0%) | |
| CIGNA | 0 (0%) | 2 (0.9%) | 2 (1.0%) | 5 (2.0%) | 0 (0%) | 2 (0.6%) | |
| TRIWEST HEALTHCARE ALLIANCE | 1 (0.5%) | 1 (0.4%) | 0 (0%) | 2 (0.8%) | 1 (0.3%) | 6 (1.9%) | |
| HUMANA MEDICARE | 0 (0%) | 0 (0%) | 3 (1.5%) | 2 (0.8%) | 4 (1.4%) | 1 (0.3%) | |
| AETNA MEDICARE | 0 (0%) | 1 (0.4%) | 0 (0%) | 1 (0.4%) | 2 (0.7%) | 5 (1.6%) | |
| HB/PB LIFETRAC | 1 (0.5%) | 2 (0.9%) | 2 (1.0%) | 2 (0.8%) | 1 (0.3%) | 1 (0.3%) | |
| REGENCE BLUE SHIELD MEDICARE | 0 (0%) | 1 (0.4%) | 0 (0%) | 3 (1.2%) | 2 (0.7%) | 3 (1.0%) | |
| HEALTHCARE MGMT ADMIN | 0 (0%) | 1 (0.4%) | 1 (0.5%) | 3 (1.2%) | 2 (0.7%) | 0 (0%) | |
| FHCC CONTRACTS | 0 (0%) | 2 (0.9%) | 2 (1.0%) | 1 (0.4%) | 1 (0.3%) | 0 (0%) | |
| KAISER FHP OF WA MEDICARE (GROUP HEALTH) | 0 (0%) | 1 (0.4%) | 3 (1.5%) | 0 (0%) | 0 (0%) | 2 (0.6%) | |
| MERITAIN HEALTH AETNA | 1 (0.5%) | 1 (0.4%) | 0 (0%) | 2 (0.8%) | 2 (0.7%) | 0 (0%) | |
| MOLINA HEALTHCARE | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 5 (1.7%) | 1 (0.3%) | |
| MONTANA MEDICAID | 0 (0%) | 1 (0.4%) | 1 (0.5%) | 0 (0%) | 1 (0.3%) | 3 (1.0%) | |
| REGENCE UMP | 1 (0.5%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 2 (0.7%) | 2 (0.6%) | |
| UNITED HEALTHCARE MEDICAID | 1 (0.5%) | 0 (0%) | 1 (0.5%) | 1 (0.4%) | 2 (0.7%) | 1 (0.3%) | |
| ZENITH AMERICAN SOLUTIONS AETNA | 0 (0%) | 1 (0.4%) | 2 (1.0%) | 2 (0.8%) | 0 (0%) | 1 (0.3%) | |
| BCBS OUT OF AREA MEDICARE | 0 (0%) | 0 (0%) | 2 (1.0%) | 1 (0.4%) | 2 (0.7%) | 0 (0%) | |
| DEPT OF LABOR AND INDUSTRIES | 0 (0%) | 2 (0.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.6%) | |
| FEP | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 3 (1.0%) | 0 (0%) | |
| KAISER FHP OF WA CORE-HIX (GROUP HEALTH) | 1 (0.5%) | 2 (0.9%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | |
| PREMERA BLUE CROSS MEDICARE | 0 (0%) | 0 (0%) | 1 (0.5%) | 2 (0.8%) | 1 (0.3%) | 0 (0%) | |
| US DEPARTMENT OF LABOR | 1 (0.5%) | 1 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.6%) | |
| FINANCIAL ASSISTANCE | 0 (0%) | 0 (0%) | 2 (1.0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | |
| MOLINA HEALTHCARE MEDICARE | 0 (0%) | 1 (0.4%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 1 (0.3%) | |
| OPTUM CARE NETWORK MEDICARE | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 1 (0.3%) | 1 (0.3%) | |
| ASURIS NORTHWEST HEALTH | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 1 (0.3%) | |
| BRITISH COLUMBIA | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 1 (0.3%) | |
| FHCC HOLDING FUNDS | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 1 (0.3%) | |
| IDAHO MEDICAID | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | |
| INTERLINK | 0 (0%) | 0 (0%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 1 (0.3%) | |
| MEDICARE ADVANTAGE GENERIC | 1 (0.5%) | 1 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | |
| PACIFICSOURCE HEALTH PLANS | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 1 (0.3%) | |
| UNITED HEALTHCARE | 0 (0%) | 1 (0.4%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) | |
| BCBS OUT OF AREA GENERIC | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 0 (0%) | |
| BCBS OUT OF AREA MEDICAID | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 0 (0%) | |
| COMMUNITY HEALTH PLAN OF WA MEDICARE | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | |
| FIRST CHOICE | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 0 (0%) | |
| GEHA | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | |
| ILWU BENEFIT PLANS | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | |
| LIFEWISE HEALTH PLAN OF WA | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 0 (0%) | |
| SELF INSURED WORKERS COMP OUT OF STATE | 0 (0%) | 0 (0%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | |
| SELF INSURED WORKERS COMP WA | 0 (0%) | 0 (0%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | |
| UNITED HEALTHCARE MEDICARE | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) | |
| UNITED HEALTHCARE WEST MEDICARE | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | |
| WA STATE HEALTH INS POOL-BMI210 | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) | |
| WELLCARE OUT OF STATE MEDICARE | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 0 (0%) | |
| WELLPOINT MEDICARE | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 0 (0%) | |
| Unknown | 0 | 0 | 0 | 1 | 1 | 0 | |
| Insurance | |||||||
| Case Rate | 72 (38%) | 102 (45%) | 65 (33%) | 82 (32%) | 91 (31%) | 102 (33%) | |
| Medicare | 53 (28%) | 38 (17%) | 53 (27%) | 74 (29%) | 70 (24%) | 77 (25%) | |
| Commercial | 42 (22%) | 51 (22%) | 29 (15%) | 51 (20%) | 78 (27%) | 75 (24%) | |
| Medicaid | 21 (11%) | 25 (11%) | 32 (16%) | 26 (10%) | 41 (14%) | 38 (12%) | |
| Other | 2 (1.0%) | 6 (2.6%) | 6 (3.0%) | 10 (3.9%) | 4 (1.4%) | 7 (2.3%) | |
| Tricare | 1 (0.5%) | 3 (1.3%) | 9 (4.5%) | 11 (4.3%) | 5 (1.7%) | 4 (1.3%) | |
| Worker's Comp | 1 (0.5%) | 3 (1.3%) | 2 (1.0%) | 0 (0%) | 0 (0%) | 4 (1.3%) | |
| Self-Pay | 0 (0%) | 0 (0%) | 2 (1.0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | |
| Unknown | 0 | 0 | 0 | 1 | 1 | 0 | |
| 1 n (%) | |||||||
theme_gtsummary_compact()
db %>%
transmute(
"Rx year" = Fiscal.year,
"Insurance company" = PAYOR_NAME,
Insurance = FINANCIAL_CLASS
) %>%
tbl_summary(
by = "Rx year",
missing = "ifany",
sort = all_categorical() ~ "frequency",
statistic = list(
all_continuous() ~ "{median} ({p25}, {p75})",
all_categorical() ~ "{n} ({p}%)"
)
)%>%
bold_labels()
| Characteristic | 2018, N = 831 | 2019, N = 2221 | 2020, N = 2061 | 2021, N = 2231 | 2022, N = 2831 | 2023, N = 3211 | 2024, N = 1331 |
|---|---|---|---|---|---|---|---|
| Insurance company | |||||||
| HB/PB BDCT | 26 (31%) | 69 (31%) | 56 (27%) | 57 (26%) | 55 (20%) | 68 (21%) | 46 (35%) |
| MEDICARE | 21 (25%) | 50 (23%) | 30 (15%) | 50 (22%) | 70 (25%) | 65 (20%) | 23 (17%) |
| HB/PB URN - OPTUM HEALTH | 3 (3.6%) | 17 (7.7%) | 20 (9.7%) | 20 (9.0%) | 16 (5.7%) | 26 (8.1%) | 7 (5.3%) |
| MOLINA HEALTHCARE MEDICAID | 5 (6.0%) | 7 (3.2%) | 18 (8.7%) | 9 (4.0%) | 18 (6.4%) | 21 (6.6%) | 4 (3.0%) |
| AETNA US HEALTHCARE | 4 (4.8%) | 15 (6.8%) | 5 (2.4%) | 19 (8.5%) | 13 (4.6%) | 12 (3.8%) | 8 (6.0%) |
| PREMERA BLUE CROSS | 7 (8.4%) | 7 (3.2%) | 7 (3.4%) | 5 (2.2%) | 15 (5.3%) | 21 (6.6%) | 4 (3.0%) |
| HEALTH NET FEDERAL SERVICES TRICARE | 0 (0%) | 4 (1.8%) | 5 (2.4%) | 9 (4.0%) | 7 (2.5%) | 8 (2.5%) | 0 (0%) |
| WA MEDICAID | 0 (0%) | 6 (2.7%) | 3 (1.5%) | 5 (2.2%) | 6 (2.1%) | 7 (2.2%) | 3 (2.3%) |
| KAISER FHP OF WA (GROUP HEALTH) | 5 (6.0%) | 2 (0.9%) | 5 (2.4%) | 2 (0.9%) | 2 (0.7%) | 8 (2.5%) | 4 (3.0%) |
| BCBS OUT OF AREA | 1 (1.2%) | 1 (0.5%) | 2 (1.0%) | 5 (2.2%) | 4 (1.4%) | 8 (2.5%) | 4 (3.0%) |
| GENERIC/UNLISTED | 1 (1.2%) | 7 (3.2%) | 8 (3.9%) | 2 (0.9%) | 0 (0%) | 2 (0.6%) | 0 (0%) |
| HB/PB INTERLINK HEALTH SERVICES | 1 (1.2%) | 3 (1.4%) | 2 (1.0%) | 3 (1.3%) | 3 (1.1%) | 7 (2.2%) | 0 (0%) |
| COMMUNITY HEALTH PLAN OF WA MEDICAID | 0 (0%) | 3 (1.4%) | 5 (2.4%) | 1 (0.4%) | 2 (0.7%) | 4 (1.3%) | 1 (0.8%) |
| GRANTS AND BUDGETS | 0 (0%) | 2 (0.9%) | 3 (1.5%) | 6 (2.7%) | 5 (1.8%) | 0 (0%) | 0 (0%) |
| REGENCE BLUE SHIELD | 0 (0%) | 2 (0.9%) | 4 (1.9%) | 0 (0%) | 3 (1.1%) | 4 (1.3%) | 3 (2.3%) |
| COORDINATED CARE CORPORATION MEDICAID | 1 (1.2%) | 3 (1.4%) | 1 (0.5%) | 1 (0.4%) | 4 (1.4%) | 4 (1.3%) | 1 (0.8%) |
| OPTUM HEALTHCARE SOLUTIONS/URN | 1 (1.2%) | 1 (0.5%) | 3 (1.5%) | 0 (0%) | 3 (1.1%) | 4 (1.3%) | 3 (2.3%) |
| BCBS OF ILLINOIS - BOEING | 0 (0%) | 3 (1.4%) | 2 (1.0%) | 1 (0.4%) | 2 (0.7%) | 4 (1.3%) | 2 (1.5%) |
| WELLPOINT MEDICAID | 0 (0%) | 3 (1.4%) | 5 (2.4%) | 0 (0%) | 2 (0.7%) | 4 (1.3%) | 0 (0%) |
| ALASKA MEDICAID | 1 (1.2%) | 0 (0%) | 0 (0%) | 3 (1.3%) | 2 (0.7%) | 4 (1.3%) | 1 (0.8%) |
| CIGNA | 0 (0%) | 1 (0.5%) | 2 (1.0%) | 3 (1.3%) | 3 (1.1%) | 1 (0.3%) | 1 (0.8%) |
| TRIWEST HEALTHCARE ALLIANCE | 0 (0%) | 1 (0.5%) | 1 (0.5%) | 1 (0.4%) | 1 (0.4%) | 5 (1.6%) | 2 (1.5%) |
| HUMANA MEDICARE | 0 (0%) | 0 (0%) | 1 (0.5%) | 2 (0.9%) | 5 (1.8%) | 2 (0.6%) | 0 (0%) |
| AETNA MEDICARE | 0 (0%) | 1 (0.5%) | 0 (0%) | 1 (0.4%) | 2 (0.7%) | 1 (0.3%) | 4 (3.0%) |
| HB/PB LIFETRAC | 0 (0%) | 2 (0.9%) | 1 (0.5%) | 2 (0.9%) | 3 (1.1%) | 1 (0.3%) | 0 (0%) |
| REGENCE BLUE SHIELD MEDICARE | 0 (0%) | 0 (0%) | 1 (0.5%) | 1 (0.4%) | 3 (1.1%) | 1 (0.3%) | 3 (2.3%) |
| HEALTHCARE MGMT ADMIN | 0 (0%) | 1 (0.5%) | 0 (0%) | 1 (0.4%) | 5 (1.8%) | 0 (0%) | 0 (0%) |
| FHCC CONTRACTS | 0 (0%) | 0 (0%) | 4 (1.9%) | 0 (0%) | 1 (0.4%) | 1 (0.3%) | 0 (0%) |
| KAISER FHP OF WA MEDICARE (GROUP HEALTH) | 0 (0%) | 0 (0%) | 4 (1.9%) | 0 (0%) | 0 (0%) | 2 (0.6%) | 0 (0%) |
| MERITAIN HEALTH AETNA | 1 (1.2%) | 1 (0.5%) | 0 (0%) | 2 (0.9%) | 1 (0.4%) | 1 (0.3%) | 0 (0%) |
| MOLINA HEALTHCARE | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 4 (1.4%) | 2 (0.6%) | 0 (0%) |
| MONTANA MEDICAID | 0 (0%) | 1 (0.5%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 4 (1.3%) | 0 (0%) |
| REGENCE UMP | 1 (1.2%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 2 (0.7%) | 2 (0.6%) | 0 (0%) |
| UNITED HEALTHCARE MEDICAID | 0 (0%) | 1 (0.5%) | 1 (0.5%) | 0 (0%) | 1 (0.4%) | 2 (0.6%) | 1 (0.8%) |
| ZENITH AMERICAN SOLUTIONS AETNA | 0 (0%) | 0 (0%) | 1 (0.5%) | 2 (0.9%) | 2 (0.7%) | 0 (0%) | 1 (0.8%) |
| BCBS OUT OF AREA MEDICARE | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.9%) | 1 (0.4%) | 2 (0.6%) | 0 (0%) |
| DEPT OF LABOR AND INDUSTRIES | 0 (0%) | 1 (0.5%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 1 (0.8%) |
| FEP | 0 (0%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 2 (0.7%) | 1 (0.3%) | 0 (0%) |
| KAISER FHP OF WA CORE-HIX (GROUP HEALTH) | 1 (1.2%) | 1 (0.5%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.8%) |
| PREMERA BLUE CROSS MEDICARE | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 3 (1.1%) | 0 (0%) | 0 (0%) |
| US DEPARTMENT OF LABOR | 1 (1.2%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 1 (0.8%) |
| FINANCIAL ASSISTANCE | 0 (0%) | 0 (0%) | 1 (0.5%) | 1 (0.4%) | 0 (0%) | 1 (0.3%) | 0 (0%) |
| MOLINA HEALTHCARE MEDICARE | 0 (0%) | 0 (0%) | 1 (0.5%) | 0 (0%) | 1 (0.4%) | 1 (0.3%) | 0 (0%) |
| OPTUM CARE NETWORK MEDICARE | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 1 (0.4%) | 0 (0%) | 1 (0.8%) |
| ASURIS NORTHWEST HEALTH | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 2 (0.6%) | 0 (0%) |
| BRITISH COLUMBIA | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 1 (0.3%) | 0 (0%) |
| FHCC HOLDING FUNDS | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 1 (0.3%) | 0 (0%) |
| IDAHO MEDICAID | 0 (0%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.8%) |
| INTERLINK | 0 (0%) | 0 (0%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.8%) |
| MEDICARE ADVANTAGE GENERIC | 0 (0%) | 1 (0.5%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| PACIFICSOURCE HEALTH PLANS | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 1 (0.3%) | 0 (0%) |
| UNITED HEALTHCARE | 0 (0%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) |
| BCBS OUT OF AREA GENERIC | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 0 (0%) |
| BCBS OUT OF AREA MEDICAID | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) |
| COMMUNITY HEALTH PLAN OF WA MEDICARE | 0 (0%) | 1 (0.5%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| FIRST CHOICE | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) |
| GEHA | 1 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| ILWU BENEFIT PLANS | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.8%) |
| LIFEWISE HEALTH PLAN OF WA | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) |
| SELF INSURED WORKERS COMP OUT OF STATE | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) |
| SELF INSURED WORKERS COMP WA | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) | 0 (0%) |
| UNITED HEALTHCARE MEDICARE | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) |
| UNITED HEALTHCARE WEST MEDICARE | 1 (1.2%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) |
| WA STATE HEALTH INS POOL-BMI210 | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) |
| WELLCARE OUT OF STATE MEDICARE | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.3%) | 0 (0%) |
| WELLPOINT MEDICARE | 0 (0%) | 0 (0%) | 0 (0%) | 0 (0%) | 1 (0.4%) | 0 (0%) | 0 (0%) |
| Unknown | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| Insurance | |||||||
| Case Rate | 30 (36%) | 91 (41%) | 79 (38%) | 82 (37%) | 77 (27%) | 102 (32%) | 53 (40%) |
| Medicare | 22 (27%) | 53 (24%) | 38 (18%) | 58 (26%) | 88 (31%) | 75 (23%) | 31 (23%) |
| Commercial | 23 (28%) | 44 (20%) | 41 (20%) | 44 (20%) | 66 (23%) | 75 (23%) | 33 (25%) |
| Medicaid | 7 (8.4%) | 25 (11%) | 33 (16%) | 20 (9.0%) | 36 (13%) | 50 (16%) | 12 (9.0%) |
| Other | 0 (0%) | 3 (1.4%) | 8 (3.9%) | 7 (3.1%) | 8 (2.8%) | 7 (2.2%) | 2 (1.5%) |
| Tricare | 0 (0%) | 4 (1.8%) | 5 (2.4%) | 9 (4.0%) | 7 (2.5%) | 8 (2.5%) | 0 (0%) |
| Worker's Comp | 1 (1.2%) | 2 (0.9%) | 1 (0.5%) | 2 (0.9%) | 0 (0%) | 2 (0.6%) | 2 (1.5%) |
| Self-Pay | 0 (0%) | 0 (0%) | 1 (0.5%) | 1 (0.4%) | 0 (0%) | 1 (0.3%) | 0 (0%) |
| Unknown | 0 | 0 | 0 | 0 | 1 | 1 | 0 |
| 1 n (%) | |||||||